Statistics play a crucial role in clinical trials and in the drug development process – from trial design to protocol development.
Biostatistics are involved in every step of clinical research including trial design, protocol development, data management and monitoring, data analysis and clinical trial reporting. There are a lot of statistical concepts that must be well known, like :
- Confidence Intervals
- Subgroup Analysis
- Parametric vs. Non-parametric statistical methods
- Sample Size Calculation
- Types of endpoints
- Statistical Reporting
- Missing Data
- Adaptive Trial Design
- Bayesian Model
PopsiCube-Fovea Team offers the following BioStatistics services:
- Consulting in statistical methodology: definition of the experimental plan, validation of the assessment criteria, determination of the sample size, drawing up of the randomization list
- Statistical analysis (SAS software, STATA, R): exploratory and confirmatory analyses, meta-analyses
- Import and integration of external data under any format
The biostatistician works closely with the rest of the biometrics team and management throughout the study including Data Managers, Statistical Programmers and Medical Writers. Regarding Data Management, our biostatisticians assist with CRF development and dataset specifications. Working with statistical programmers, methodological biostatisticians ensure data formatting is correct and select data to be pooled. In terms of medical writing, biostatisticians will often write the statistical part of the Clinical Study Report.
In the Statistical Analysis Plan (SAP), the biostatistician will outline study endpoints, sample size calculation, interim analysis planning and the hypothesis and testing procedures.
The most well-known responsibility of the biostatistician is the definition of sample size which involves several factors that influence the size of the study, timelines and budget requirements.
The most poorly-known responsibility of the biostatistician is the definition of the comparator which could involve published study meta analysis.